Why high dropout on the conditional embedding
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question
- Dominant language
- Python
- Stars
- 159
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
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Hi dear teams,
Thank you so much for the wonderful work. It's surprising that the dropout on the conditional embedding is recommended to set very high. Could you please help to explain some insights behind this choice, and what's effect when setting the conditional embedding dropout to high / low value seperately?
Best,
Xinyu
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Research direction
No file or test is named. Start by locating the conditional embedding dropout configuration and its surrounding model documentation, then document how high and low values affect the model and why a high value is recommended.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100